High accuracy indoor visible light positioning using a long short term memory-fully connected network based algorithm
Hongyao Chen, Wei Han, Jianping Wang, Huimin Lu, Danyang Chen, Jianli Jin, Lifang Feng
Abstract
In this work, a novel positioning algorithm based on a long short term memory-fully connected network (LSTM-FCN) is proposed to improve the performance of an indoor visible light positioning (VLP) system. Using the proposed LSTM-FCN based positioning algorithm, the VLP system with a single light emitting diode (LED) and multiple photodetectors (PDs) was implemented. On this basis, the positioning performance of the established VLP system using proposed LSTM-FCN, traditional FCN and support vector regression (SVR) based algorithm is investigated and compared. It is demonstrated that the VLP system using the proposed LSTM-FCN based algorithm has better performance than that using other machine learning algorithms. As a result, an average positioning error of 0.92 cm and a maximum positioning error of less than 5 cm can be obtained for the established VLP system.